Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Adeel Akram¹, Tallha Akram², Ghada Atteia^3,*, Ayman Qahmash⁴, Sultan Alanazi⁵, Faisal Mohammad Alotaibi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2311-2337, 2025, DOI:10.32604/cmes.2025.069697 - 26 November 2025

Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score… More >

Mohamed Hammad^1,2,*, Mohammed ElAffendi¹, Souham Meshoul^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1055-1086, 2025, DOI:10.32604/cmes.2025.072650 - 30 October 2025

Abstract Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy. Traditional diagnostic methods often rely on subjective visual assessments, which can lead to misdiagnosis. This study addresses these challenges by developing HybridFusionNet, a novel model that integrates Convolutional Neural Networks (CNN) with 1D feature extraction techniques to enhance diagnostic accuracy. Utilizing two extensive datasets, BCN20000 and… More >

Kun Lan¹, Feiyang Gao¹, Xiaoliang Jiang^1,*, Jianzhen Cheng^2,*, Simon Fong³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4805-4824, 2025, DOI:10.32604/cmc.2025.065864 - 30 July 2025

Abstract With the continuous development of artificial intelligence and machine learning techniques, there have been effective methods supporting the work of dermatologist in the field of skin cancer detection. However, object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations, such as bubbles and scales. To address these challenges, we propose a dual U-Net network framework for skin melanoma segmentation. In our proposed architecture, we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net. First, we establish… More >

Tallha Akram^1,*, Fahdah Almarshad¹, Anas Alsuhaibani¹, Syed Rameez Naqvi^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079 - 30 May 2025

Abstract Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification… More >

Armughan Ali^1,2, Hooria Shahbaz², Robertas Damaševičius^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1367-1398, 2025, DOI:10.32604/cmc.2025.059301 - 26 March 2025

Abstract Skin cancer is the most prevalent cancer globally, primarily due to extensive exposure to Ultraviolet (UV) radiation. Early identification of skin cancer enhances the likelihood of effective treatment, as delays may lead to severe tumor advancement. This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis, with an architecture that integrates a Vision Transformer, a bespoke convolutional neural network (CNN), and an Xception module. They were evaluated using two benchmark datasets, HAM10000 and Skin Cancer ISIC. On the HAM10000, the model achieves a precision of 95.46%, an… More >

Kun Liu¹, Chen Bao^1,*, Sidong Liu²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053 - 06 March 2025

Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >

Khadija Manzoor¹, Fiaz Majeed², Ansar Siddique², Talha Meraj³, Hafiz Tayyab Rauf^4,*, Mohammed A. El-Meligy⁵, Mohamed Sharaf⁶, Abd Elatty E.Abd Elgawad⁶

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1459-1459, 2025, DOI:10.32604/cmc.2024.061588 - 03 January 2025

Abstract This article has no abstract. More >

VIRGINIA LOTTI¹, GAETANO DE SIENA², STEFANO BACCI^3,*

BIOCELL, Vol.48, No.12, pp. 1751-1759, 2024, DOI:10.32604/biocell.2024.057904 - 30 December 2024

Abstract Background: Impaired wound healing is one of the most well-known complications of type 2 diabetes mellitus. Experimental evidence suggested that treatment with Exendin-4, a glucagon-like peptide-1 agonist displaying a wide range of antidiabetic effects, can promote tissue regeneration. Objectives: Thus, this study aimed to examine the efficacy of topical treatment with Exendin-4 in accelerating wound healing in normoglycemic and hyperglycemic mice. Methods: For this purpose, two wounds inflicted on the back skin of 12 normo- and 12 hyperglycemic mice were injected intradermally with either saline solution or Exendin-4. Wounds were collected at the time of abrasion… More >

Muhammad Suleman¹, Faizan Ullah¹, Ghadah Aldehim^2,*, Dilawar Shah¹, Mohammad Abrar^1,3, Asma Irshad⁴, Sarra Ayouni²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365 - 26 December 2023

Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from… More >